Release: full pipeline + evaluation + dashboard (v0.1.0)

README.md

@@ -9,35 +9,198 @@ potential hallucinations.
 
 > Course project · MICS · Principles of Software Development (S2 2025–26).
 
-## Status
+---
 
-Scaffold. Features land via pull requests — see the open issues and `docs/`.
+## Contents
 
-## Documentation
+- [What SumLens does](#what-sumlens-does)
+- [Hardware requirements](#hardware-requirements)
+- [Installation](#installation)
+- [Running the app](#running-the-app)
+- [Usage walkthrough](#usage-walkthrough)
+- [Interpreting the results](#interpreting-the-results)
+- [Exporting results](#exporting-results)
+- [Development](#development)
 
-- [`docs/requirements.md`](docs/requirements.md) — functional / non-functional requirements, MoSCoW, user stories, traceability.
-- [`docs/data-model.md`](docs/data-model.md) — canonical data types.
-- [`docs/research-plan.md`](docs/research-plan.md) — signals, fusion, evaluation methodology.
+---
 
-## Development
+## What SumLens does
+
+SumLens takes a text document (pasted or PDF) and:
+
+1. Summarises it locally using BART (`facebook/bart-large-cnn`) — no external API.
+2. Scores each summary sentence against the source using three signals:
+   - **Signal A — Classifier:** LettuceDetect flags hallucinated tokens.
+   - **Signal B — NLI:** DeBERTa-v3 checks whether atomic claims are entailed by the source.
+   - **Signal C — Attribution:** Inseq integrated gradients measure how much each source span influenced each summary sentence.
+3. Fuses the signals into a single grounding score (0 = hallucinated, 1 = grounded).
+4. Labels each sentence: **grounded**, **weakly grounded**, or **hallucinated**.
+5. Displays the result as a colour-coded summary with click-to-highlight source spans.
+
+---
+
+## Hardware requirements
+
+The pipeline loads three large transformer models. Running on CPU is supported but
+can take several minutes per document.
 
-Requires Python 3.11+.
+| Setup | RAM | VRAM | Expected time |
+|-------|-----|------|---------------|
+| GPU (recommended) | 16 GB | 8 GB+ | ~30–60 s |
+| CPU-only | 16 GB | — | 3–10 min |
+
+Models are downloaded automatically from Hugging Face on first run (~4 GB total).
+No paid API key is required.
+
+---
+
+## Installation
+
+Requires **Python 3.11+** and **Git**.
 
 ```bash
+git clone https://github.com/bacemtayeb/SumLens.git
+cd SumLens
 python3.11 -m venv .venv
+
+# Windows
+.venv\Scripts\activate
+# macOS / Linux
 source .venv/bin/activate
+
 pip install -e ".[dev]"
 python -m nltk.downloader punkt punkt_tab
 ```
 
-### Quality gate (CI enforces this on every PR)
+---
+
+## Running the app
+
+```bash
+python app.py
+```
+
+Gradio prints a local URL, e.g.:
+
+```
+Running on local URL: http://127.0.0.1:7860
+```
+
+Open that URL in your browser. The app runs entirely on your machine — no data
+leaves your computer.
+
+---
+
+## Usage walkthrough
+
+### Step 1 — Load a document
+
+You have two options:
+
+- **Paste text** — click the *Paste text* box and type or paste your document
+  (up to 10 000 words).
+- **Upload a PDF** — click *or upload PDF* and select a file (up to 5 MB).
+
+If you provide both, the PDF takes priority.
+
+### Step 2 — Analyse
+
+Click the **Analyse** button. The button is disabled while the pipeline runs
+(typically 30–60 s on GPU, longer on CPU). Both export buttons appear once
+analysis is complete.
+
+### Step 3 — Read the summary
+
+The right panel shows the summary with each sentence colour-coded:
+
+| Colour | Label | Meaning |
+|--------|-------|---------|
+| Green | Grounded | Well-supported by the source |
+| Orange | Weakly grounded | Partial support; treat with caution |
+| Red | Hallucinated | Low support; likely fabricated or distorted |
+
+### Step 4 — Trace a sentence to the source
+
+Click any summary sentence. The left panel highlights (in yellow) the source
+sentences most strongly attributed to it by the model. Click a different summary
+sentence to switch the highlight.
+
+### Step 5 — Adjust the thresholds (optional)
+
+Two sliders let you change the decision boundaries without re-running the model:
+
+- **τ hallucinated** (default 0.30) — sentences with a grounding score *below*
+  this value are labelled hallucinated.
+- **τ grounded** (default 0.70) — sentences with a grounding score *above* this
+  value are labelled grounded. Anything in between is weakly grounded.
+
+Move either slider and the summary colours update instantly.
+
+---
+
+## Interpreting the results
+
+- The **grounding score** (0–1) represents how strongly the model believes a
+  summary sentence is supported by the source. It is not a probability in a strict
+  statistical sense — treat it as a relative risk indicator.
+- A **hallucinated** label does not guarantee the sentence is wrong; it means the
+  model could not find sufficient evidence in the source text. Always cross-check
+  flagged sentences manually.
+- The **signal breakdown** (JSON export) shows the individual classifier, NLI, and
+  attribution scores for each sentence, which can help diagnose *why* a sentence
+  was flagged.
+
+---
+
+## Exporting results
+
+Two download buttons appear after analysis:
+
+- **Export JSON** — downloads the full `AnalysisResult` as a JSON file. The schema
+  matches `sumlens/types.py` and round-trips via `AnalysisResult.model_validate()`.
+- **Export PDF** — downloads a human-readable PDF containing the colour-annotated
+  summary, a legend, and a per-sentence signal-scores table.
+
+---
+
+## Development
+
+### Quality gate (CI enforces on every PR)
 
 ```bash
-ruff check . && mypy sumlens tests && pytest -q --cov=sumlens --cov-fail-under=70
+ruff check . && mypy sumlens tests app.py && pytest -q --cov=sumlens --cov-fail-under=70
 ```
 
-Lint (ruff), type-check (mypy, strict), and tests with a ≥70% coverage gate must
-pass before any PR is merged to `main`.
+Lint (ruff), strict type-check (mypy), and tests with a ≥ 70 % coverage gate
+must pass before any PR is merged.
+
+### Project layout
+
+```
+sumlens/
+  types.py          # canonical data model (AnalysisResult, etc.)
+  ingest.py         # PDF / text → Document
+  summarise.py      # BART summarisation
+  signals/
+    classifier.py   # Signal A — LettuceDetect
+    nli.py          # Signal B — DeBERTa NLI
+    attribution.py  # Signal C — Inseq attribution
+  fuse.py           # logistic-regression fusion + Platt calibration
+  pipeline.py       # orchestrates ingest → summarise → signals → fuse
+app.py              # Gradio UI entry point
+tests/              # pytest suite (all models mocked)
+docs/               # requirements, data model, research plan
+```
+
+### Documentation
+
+- [`docs/requirements.md`](docs/requirements.md) — functional / non-functional requirements, MoSCoW, user stories, traceability.
+- [`docs/data-model.md`](docs/data-model.md) — canonical data types and JSON schema.
+- [`docs/research-plan.md`](docs/research-plan.md) — signals, fusion, evaluation methodology.
+- [`docs/mockup.html`](docs/mockup.html) — static HTML wireframe of the two-panel dashboard UI (open in any browser).
+- [`docs/use-case.puml`](docs/use-case.puml) — PlantUML use-case diagram (UC-01 "Verify a Summary").
+
+---
 
 ## License